In conventional particle image analyses, morphological tests on cells in blood and particles in urine are performed through a procedure in which a laboratory personnel prepares a sample on a microscope slide and directly observes it through a microscope. Such microscopic tests have problems that the personnel's ability affects the results, the tests are time consuming, and so on, and are therefore desired to be made more efficient.
In recent years, with the advancement in automation of testing, Patent Documents 1 and 2, for example, have disclosed flow type particle image analysis devices in which: a sample is caused to flow through a flow cell having a special shape; particles in the sample are caused to flow through a wide imaging region; magnified images of the particles in the sample are captured as static images while a flash lamp is turned on; and the particles are classified based on the static images. The flow type particle image analysis devices create a flat flow of a sample liquid by causing it to flow through the flow cell having a special shape while surrounding the sample liquid with the flow of a sheath liquid on an outer side thereof. There are, however, cases where the shape of the flow of the sample liquid becomes thicker than a preset value due to such a reason that a predetermined amount of sheath liquid is not flowing or the flow of the sheath liquid is uneven. As the flow of the sample liquid becomes thicker, the positions of the particles are displaced from the in-focus position, making it impossible to obtain proper images in some cases.
Static images of particles need to be captured always at the in-focus position in order for the flow type particle image analysis devices to maintain the accuracy of their particle classification. To always obtain in-focus images, the following processes need to be performed, for example.
(1) The in-focus position is accurately adjusted at the time of the startup of the device.
(2) The in-focus position and the thickness of the flow of the sample liquid are regularly checked after (1) as well.
Focal adjustment methods regarding (1) are described in Patent Documents 3 to 5, for example. Patent Document 3 discloses a method in which in the adjustment of the focal point, static images of standard particles made of the same substance and having the same size (hereinafter, standard particles) are obtained at mutually different multiple positions by moving the flow cell or the field lens; the average area value of the standard particles at each of the positions is calculated; and the position of the flow cell or the field lens is adjusted to a position at which the value is smallest. Patent Document 4 discloses a focal adjustment method using a neural network. In this method, the neural network learns feature parameters (density covariance, density contrast, density derivative, etc.) of standard particle images at multiple focal positions in advance. The learned results are loaded in the device, and the focal adjustment is performed on the basis thereof. Patent Document 5 discloses a method using a covariance value in an R image of the standard particle, instead of the area used in Patent Document 3. This is based on a nature that the standard particle at the in-focus position appears in the middle between a white, shining state and a dark state. In the disclosed method, the position of the flow cell or the field lens is adjusted to a position at which the density covariance (covariance) is equal to a substantially center value between the largest and smallest values.
A method of checking the in-focus position and the thickness of the flow of the sample liquid regarding (2) is described in Patent Document 5, for example. Patent Document 5 discloses a method in which the displacement from the in-focus point and the thickness of the flow of the sample liquid are checked by using distribution of the aforementioned covariance values in a relationship diagram between the covariance values and sample widths (the horizontal direction of the static images). It is judged as out of focus when the covariance values are different from the value at the in-focus position. The flow of the sample liquid is judged as thicker when the dispersion of the covariance values (the distribution of the covariance values) is large.